The objective of the proposed research is to determine how the activity of neurons in the substantia nigra pars reticulata (SNr), one of the major output nuclei of the basal ganglia, is controlled by synaptic input. Simple network models of basal ganglia function and disorders assume that the activity of output neurons is determined by summing the amount of inhibitory and excitatory inputs received. It is clear, however, that single neurons have active intrinsic mechanisms by which synaptic inputs may be integrated in a highly complex non-linear fashion. These complex properties of synaptic integration will be examined in SNr neurons by combining in vitro whole cell recording, extracellular recording and computational modeling. First the passive and then the active properties of these neurons will be catalogued using whole-cell recordings in rat brain slices. These experiments will use current and voltage-clamping in conjunction with pharmacological blockade of various voltage- and ligand gated channels to isolate and characterize purely passive membrane properties and specific voltage-dependent conductances. Recorded neurons will be intracellularly stained and reconstructed histologically with Neurolucida, and the quantitative morphometric data obtained will be used along with the electrophysiological data to construct a compartmental model of SNr neurons. The model will be adjusted and fine tuned by comparing the behavior of the model to that of SNr neurons in whole cell recordings in vitro and in extracellular single unit recordings in vivo, while constraining the parameters to those obtained in the recording experiments. To study the mechanisms by which synaptic input controls activity, the parameters of synaptic inputs including the time courses and amplitudes of excitatory and inhibitory inputs will be measured and used in the model. Finally, realistic sequences of synaptic input, inferred from in vivo and in vitro recordings of SNr neurons will be input to the model to determine the input-output function of SNr neurons.